G-Search MCP
The G-Search MCP server enables efficient, parallelized Google searches with multiple keywords and structured result outputs.
Parallel Searching: Perform simultaneous searches with multiple keywords
Structured Data Output: Return search results in JSON format for easy processing
Configurable Parameters: Customize settings including result limits, timeout durations, locale preferences, and state saving options
Automatic Verification Handling: Detect and handle CAPTCHA challenges when they appear
User Behavior Simulation: Mimic real user browsing patterns to avoid detection
Browser Optimization: Utilize multiple tabs within a single browser instance
Debug Mode: Enable browser visibility for debugging or verification purposes
Integration Capabilities: Configure and integrate with desktop applications
Enables parallel searching with multiple keywords simultaneously on Google, returning structured search results with configurable parameters for result limits, timeout settings, and locale preferences.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@G-Search MCPsearch for 'best AI tools 2024' and 'machine learning frameworks' with 15 results each"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
G-Search MCP
A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously.
This project is modified from google-search.
🌟 Recommended: OllaMan - Powerful Ollama AI Model Manager.
Advantages
Parallel Searching: Supports searching with multiple keywords on Google simultaneously, improving search efficiency
Browser Optimization: Opens multiple tabs in a single browser instance for efficient parallel searching
Automatic Verification Handling: Intelligently detects CAPTCHA and enables visible browser mode for user verification when needed
User Behavior Simulation: Simulates real user browsing patterns to reduce the possibility of detection by search engines
Structured Data: Returns structured search results in JSON format for easy processing and analysis
Configurable Parameters: Supports various parameter configurations such as search result limits, timeout settings, locale settings, etc.
Related MCP server: Enhanced Google Search MCP
Quick Start
Run directly with npx:
npx -y g-search-mcpFirst time setup - install the required browser by running the following command in your terminal:
npx playwright install chromiumDebug Mode
Use the --debug option to run in debug mode (showing browser window):
npx -y g-search-mcp --debugConfigure MCP
Configure this MCP server in Claude Desktop:
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"g-search": {
"command": "npx",
"args": ["-y", "g-search-mcp"]
}
}
}Features
search- Execute Google searches with multiple keywords and return resultsUses Playwright browser to perform searches
Supports the following parameters:
queries: Array of search queries to execute (required parameter)limit: Maximum number of results to return per query, default is 10timeout: Page loading timeout in milliseconds, default is 60000 (60 seconds)noSaveState: Whether to avoid saving browser state, default is falselocale: Locale setting for search results, default is en-USdebug: Whether to enable debug mode (showing browser window), overrides the --debug flag in command line
Example usage:
Use the search tool to search for "machine learning" and "artificial intelligence" on GoogleExample response:
{
"searches": [
{
"query": "machine learning",
"results": [
{
"title": "What is Machine Learning? | IBM",
"link": "https://www.ibm.com/topics/machine-learning",
"snippet": "Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy."
},
...
]
},
{
"query": "artificial intelligence",
"results": [
{
"title": "What is Artificial Intelligence (AI)? | IBM",
"link": "https://www.ibm.com/topics/artificial-intelligence",
"snippet": "Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind."
},
...
]
}
]
}Usage Tips
Handling Special Website Scenarios
Adjusting Search Parameters
Search Result Quantity: For more search results:
Please return the top 20 search results for each keywordThis will set the
limit: 20parameter.Increase Timeout Duration: For slow loading situations:
Please set the page loading timeout to 120 secondsThis will adjust the
timeoutparameter to 120000 milliseconds.
Locale Settings Adjustment
Change Search Region: Specify a different locale setting:
Please use Chinese locale (zh-CN) for searchingThis will set the
locale: "zh-CN"parameter.
Debugging and Troubleshooting
Enable Debug Mode
Dynamic Debug Activation: To display the browser window during a specific search operation:
Please enable debug mode for this search operationThis sets
debug: trueeven if the server was started without the--debugflag.
Installation
Prerequisites
Node.js 18 or higher
NPM or Yarn
Install from Source
Clone the repository:
git clone https://github.com/jae-jae/g-search-mcp.git
cd g-search-mcpInstall dependencies:
npm installInstall Playwright browser:
npm run install-browserBuild the server:
npm run buildDevelopment
Auto Rebuild (Development Mode)
npm run watchUsing MCP Inspector for Debugging
npm run inspectorRelated Projects
fetcher-mcp: A powerful MCP server for fetching web page content using Playwright headless browser. Features intelligent content extraction, parallel processing, resource optimization, and more, making it an ideal tool for web content scraping.
License
Licensed under the MIT License
Available Tools
1 toolsearchC
Search on Google for multiple keywords and return the results
| Name | Required | Description | Default |
|---|---|---|---|
| debug | No | Whether to enable debug mode (showing browser window), overrides the --debug command line flag if specified | |
| limit | No | Maximum number of results to return per query (default: 10) | |
| locale | No | Locale setting for search results (default: en-US) | |
| noSaveState | No | Whether to avoid saving browser state (default: false) | |
| queries | Yes | Array of search queries to perform | |
| timeout | No | Page loading timeout in milliseconds (default: 60000) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the action (search Google) and output (return results) but omits critical behavioral details like rate limits, authentication requirements, error handling, or what format results are returned in. This leaves significant gaps for an AI agent to understand tool behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place with no redundant information. The structure is front-loaded with the essential action and purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what format results are returned in, how errors are handled, or provide context about the search operation's limitations. The agent would need to guess about important behavioral aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing comprehensive parameter documentation. The description adds minimal value beyond the schema, only implying that 'queries' parameter contains search keywords. No additional semantic context is provided about parameter interactions or usage patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search on Google for multiple keywords and return the results.' It specifies the verb (search), resource (Google), and scope (multiple keywords). However, with no sibling tools mentioned, there's no opportunity to differentiate from alternatives, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, constraints, or typical use cases. While there are no sibling tools to contrast with, the description lacks any contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion or overlap between tools, making disambiguation perfect. The single tool has a clear and distinct purpose without any ambiguity.
With only one tool, naming consistency is inherently perfect as there are no other tools to compare against. The tool name 'search' follows a simple verb pattern, which is appropriate for its function.
A single tool is too few for a search server, as it lacks basic operations like refining searches, handling different result types, or managing search history. This minimal set feels thin and incomplete for the apparent scope of a search service.
The tool set is severely incomplete for a search domain, offering only a basic search function without any support for advanced features like filtering, pagination, or accessing specific result details. This will likely cause agent failures when more complex search tasks are required.
Maintenance
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